VineForecast App
A farm management and disease forecasting app used by wine makers throughout the season to digitize field work and support data-driven decisions around vine health and funcgide reduction.
Visit VineForecastClimate Physicist · Data Scientist · Python Developer · Founder
I am a climate physicist based in Berlin working at the intersection of software development, AI, weather and agriculture.
I am a Python developer and climate physicist building machine learning applications for weather and agriculture. As co-founder of VineForecast, I have developed digital tools for sustainable viticulture with a technical focus on backend and data engineering.
My work spans backend development with Django (REST) and PostgreSQL, large-scale weather data processing with xarray and zarr, and the development of mechanistic and AI-based plant models using NumPy and JAX. I place strong emphasis on clean, maintainable code and test-driven development.
Before founding VineForecast, I developed a strong interest in research through my studies in climate physics and complex systems. My published work includes research on traffic jams and machine-learning-based forecasting of El Niño.
The VineForecast GmbH develops digital tools for agriculture, combining meteorology, agronomy and AI.
A farm management and disease forecasting app used by wine makers throughout the season to digitize field work and support data-driven decisions around vine health and funcgide reduction.
Visit VineForecastData-driven harvest forecasting for leafy crops, built from sparse, irregular, and noisy field data. It combines meteorological data, variety embeddings, with advanced AI methods, such as neural ordinary differential equations.
Visit CultivisionOne-parametric bifurcation analysis of data-driven car-following models.
Physica D: Nonlinear Phenomena, 427, 133016. (2021) — 10.1016/j.physd.2021.133016
Probabilistic forecasting of El Niño using neural network models.
Geophysical Research Letters, 47(6). (2020) — 10.1029/2019GL086338
The application of machine learning techniques to improve El Niño prediction skill.
Frontiers in Physics, 7, 153. (2019) — 10.3389/fphy.2019.00153
Subgrid-scale variability in clear-sky relative humidity and forcing by aerosol–radiation interactions in an atmosphere model.
Atmospheric Chemistry and Physics, 18(12), 8589–8599. (2018) — 10.5194/acp-18-8589-2018
A Python library for retrieving passport data from the Vitis International Variety Catalogue (VIVC).
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